Intelligent Risk Control Solution for Large Campus Events

Pain Points

When organizing large-scale events (such as school anniversaries, sports meets, academic conferences, and cultural performances), universities currently face the following core pain points in their application and risk management processes:

1. Cumbersome Application Process, Low Efficiency: Traditional event applications rely on paper forms or simple OA systems, requiring sequential approvals from multiple departments such as the Security Office, Student Affairs Office, Logistics Department, and University Office. The process is lengthy. Statistics show that a medium-sized event takes an average of 5-7 working days to complete approval, severely hindering event preparation efficiency.

2. Lagging Risk Identification, Reliance on Manual Experience: Safety risk assessments mostly depend on manual experience, lacking systematic and data-driven tools. It is difficult to achieve early warnings and quantitative assessments for key risk points such as event scale, venue capacity, crowd density, weather factors, and equipment safety. Hazards are often only discovered after an incident occurs.

3. Difficult Cross-Departmental Collaboration, Severe Information Silos: Event application information is scattered across different departmental systems, lacking a unified data sharing and collaboration platform. Departments like Security, Logistics, and Publicity operate independently, leading to untimely and inaccurate information transfer. This often results in issues like "multiple applications, duplicate approvals" or "missing key information."

4. Lack of Emergency Plans, Insufficient Response Capability: Most events lack digital emergency plans based on risk scenarios. In the event of an emergency (such as a crowd crush, fire, or extreme weather), on-site command and dispatch rely on manual communication, resulting in slow response times and low handling efficiency, making it difficult to ensure the safety of faculty and students.

5. Insufficient Data Accumulation, Difficulties in Review and Improvement: After an event, relevant data (such as participant numbers, risk incidents, and handling records) lacks systematic archiving and analysis. This prevents the use of experience for future events, leading to the recurrence of similar problems.

Solution Overview

This solution is built on the core concept of "AI-driven, Process Re-engineering, Proactive Risk Management, Collaborative Closed Loop," aiming to create an intelligent application and risk control platform covering the entire event lifecycle. By integrating AI technologies such as Natural Language Processing (NLP), Knowledge Graphs, and Multimodal Data Analysis, it upgrades traditional passive and fragmented event management into an active and systematic intelligent governance system.

Overall Architecture: The solution adopts a "1+3+N" architecture – 1 unified intelligent hub (AI Decision Engine), 3 core capability platforms (Intelligent Application, Risk Control, Emergency Collaboration), and N business scenario applications (e.g., large gatherings, sports events, academic forums).

Design Philosophy: Starting from the event initiation, the AI automatically parses the application content and generates a structured risk checklist. The approval process incorporates intelligent recommendations and automatic validation to shorten the cycle. During the execution phase, IoT devices and AI video analysis monitor risks in real-time. After the event, a review report is automatically generated, forming a management closed loop.

Unique Value: Unlike traditional "point solutions" like OA or security systems, this solution achieves intelligent integration across the entire chain of "Application - Approval - Execution - Review." It shifts risk control from "post-event remediation" to "pre-event prevention," significantly reducing the incidence of campus safety incidents.

Solution Components

This solution is organically composed of the following core components, which work together to form a complete solution:

• Intelligent Application Engine: Based on NLP technology, it automatically parses key information from event application forms (e.g., event type, scale, time, location, participants), generates structured data, and intelligently matches the approval process and required materials. It supports dual entry points via mobile and PC, enabling "one-click application, automatic routing."

• Intelligent Risk Assessment Module: Utilizes Knowledge Graphs and historical data to build a campus event risk model. It conducts multi-dimensional risk assessments for each event (e.g., crowd density, venue capacity, weather impact, equipment safety), outputs risk levels and early warning suggestions, and assists in approval decisions.

• Cross-Departmental Collaboration Workbench: A unified portal integrates approval nodes from departments like Security, Student Affairs, Logistics, and Publicity, supporting complex processes such as parallel approval, joint sign-off, and task transfer. It features built-in message push and pending task reminders to ensure real-time information synchronization and eliminate information silos.

• AI Video Analysis and IoT Monitoring: During the event execution phase, it connects to existing campus cameras and IoT sensors (e.g., people counters, temperature/humidity sensors, smoke detectors). AI algorithms monitor crowd density, abnormal behavior, environmental changes, etc., in real-time and automatically trigger alerts.

• Digital Emergency Plans and Command Dispatch: Pre-sets multiple emergency plans based on risk scenarios (e.g., evacuation, medical rescue, fire linkage), supporting one-click activation. Combined with GIS maps and personnel positioning, it enables visualized command and dispatch, improving emergency response efficiency.

• Data Insights and Review Reports: After an event, it automatically aggregates application data, approval records, risk incidents, handling logs, etc., to generate multi-dimensional review reports. Through trend analysis and comparison, it provides data support for school management decisions.

• Training and Operational Support: Provides tiered training courses for administrators, approvers, and event organizers, along with 7x24 technical support to ensure smooth implementation and continuous optimization of the solution.

Implementation Roadmap

This solution adopts a "phased, incremental" implementation strategy to ensure a smooth transition and manageable risks. The recommended total duration is 6-8 months, detailed as follows:

Phase	Objective	Key Activities	Milestone	Estimated Duration
Phase 1: Foundation Setup	Complete core platform deployment and data integration	1. Deploy Intelligent Application Engine & Collaboration Workbench 2. Integrate with existing school OA, Academic Affairs, and Security systems 3. Configure basic approval workflows and permission system	Platform launch, supporting basic application and approval functions	2 months
Phase 2: AI Capability Injection	Implement intelligent risk assessment and early warning	1. Train risk model (based on historical data & expert rules) 2. Integrate AI Video Analysis module 3. Connect IoT device data	Risk module launch, supporting automatic assessment and early warning	2 months
Phase 3: Emergency & Review	Enhance emergency command and data insight capabilities	1. Deploy Digital Emergency Plan module 2. Configure GIS maps and personnel positioning 3. Launch Review Report function	Emergency and review functions available	1.5 months
Phase 4: Optimization & Promotion	System tuning and campus-wide rollout	1. Collect user feedback, iterate and optimize 2. Conduct campus-wide training and promotion 3. Establish operational management system	System stable, covering all campus events	1.5 months

Risk Management: A review will be conducted after each phase, and the plan for the next phase will be adjusted based on feedback. A dedicated project team, led by university leadership, will be established to ensure smooth cross-departmental coordination.

Expected Outcomes

Through the implementation of this solution, significant results are expected in the following areas:

Short-Term Outcomes (1-3 months)

• Event application approval cycle shortened by over 60%, from an average of 5-7 working days to within 2 working days.
• Cross-departmental collaboration efficiency improved by 50%, reducing redundant communication and information omission.
• Risk identification coverage rate increased to over 90%, reducing reliance on manual experience.

Long-Term Value (6-12 months)

• Safety incident rate for large campus events reduced by over 70% (based on projections from similar project data).
• Emergency response time shortened to within 5 minutes, handling efficiency improved by 80%.
• Formation of a reusable event risk knowledge base, providing continuous data support for school safety management.
• Estimated annual savings of [To be determined] in labor costs for the school, and reduction of potential losses caused by safety incidents.

Comparison of Effects:

Indicator	Before Implementation	After Implementation
Approval Cycle	5-7 days	<2 days
Risk Warning Coverage	<30%	>90%
Emergency Response Time	>15 minutes	<5 minutes
Safety Incident Rate	Baseline	Reduced by 70%

Reference Cases

The following are successful cases in similar scenarios for reference:

1. "Smart Campus Security Control Platform" Project at a Top 985 University: To address the management pain points of large events (e.g., school anniversaries, sports meets), an intelligent application and risk warning system was deployed. Post-implementation, event approval efficiency improved by 65%, and the number of safety incidents during large events that year was zero. The project was awarded the "Safe Campus" demonstration project title by the Ministry of Education.

2. "Integrated Event Risk Control Platform" for a Provincial Education Group: Covering over 20 affiliated primary and secondary schools, it uses AI video analysis and IoT monitoring to achieve real-time crowd density warnings during events. Within one year of launch, it successfully warned and handled 3 potential crowd crush incidents, ensuring the safety of tens of thousands of students and teachers.

3. "Intelligent Security Command System" for a Major Sports Event: Although not a campus scenario, its AI-based risk assessment and emergency dispatch logic hold significant reference value. The system achieved the goal of "zero major safety incidents" during the event, reducing emergency response time to 3 minutes.

Note: The above cases are compiled based on public information, and specific data has been desensitized.